Ahmed Alaa
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This is true if the noise samples are independent. This happens is input noise is white (as normally is assumed).I know that if the transversal filters coefficents used for equalization are: alpha i, then the SNR decreases by a factor = summation(alpha i ^ 2), what if this summation is less than 1 ? i.e. what if the filter coefficients used to equalize are too small ?
Depends on your definition of SNR. Signal and noise are spectral distributions, but SNR may be defined as scalar quantity, ratio of signal power total power to noise total power.SNR is the same at the input of the equalizer and the output, because both signal and noise are multiplied (or divided) by the same constant.
Depends on your definition of SNR. Signal and noise are spectral distributions, but SNR may be defined as scalar quantity, ratio of signal power total power to noise total power.
I was wrong. The amplitude of the signal sample involves the samples of the response of the channel too: it is summation(alpha_i*h_-i). (h_i are the samples of the impulse response of the channel). Sorry.If I'm not wrong, the amplitude of the signal sample at the equalizer output is increased by approximately [summation(alpha_i)] if ISI is not strong.
It is right that noise power increases as summation(alpha_i^2). But signal changes too:Ahmed said:Btw, I am calculating summation(alpha i) -> (the sum of filter coefficients squared which represents the amount of SNR loss) and for some channels it is +ve (in dB) which means that I'll loose SNR, and for other channels it is -ve (which means I'll gain SNR)
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